{"id":3679111,"url":"https://alion.io/job/bayer-polska-lead-data-architect","title":"Lead Data Architect","company":{"id":2189753,"name":"Bayer Polska","domain":"bayer.com.pl","url":"https://alion.io/company/bayer-com-pl","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":25760,"max":32200,"currency":"PLN","period":"month","gross":null,"usd_annual":98004},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GraphQL","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Terraform","optional":false},{"name":"Trunk-Based Development","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GDPR","optional":true},{"name":"GraphRAG","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-03T08:49:05Z","employer_posted_date":null,"last_verified_at":"2026-11-03T08:49:05Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"As a Lead Data Architect, you are part of Bayer's Data & AI Team with a focus on creating data solutions that provide timely, high-quality data to scientists, analysts, and AI agents. You will lead the technical design and implementation of GxP-validated data solutions for clinical studies in our Clinical Data Lakehouse, combining a solid understanding for the clinical data flow with excellent technical and conceptual skills. Zakres obowiązków\nLead the design and implementation of fit-for-purpose data solutions to store, transform, share and analyze clinical data, enabling an efficient clinical dataflow in compliance to legal and regulatory requirements, including appropriate measures to protect secret data in randomized double-blinded clinical studies in accordance to GxP. \nCollaborate closely with relevant stakeholders in an agile setup to capture, specify and address data-related requirements and dependencies to ensure a fit-for-purpose technical implementation in a healthy data ecosystem. \nActively exchange with peers, embracing and applying best-practices to maintain a coherent, consolidated and scalable data landscape. \nLead the design and implementation of integration patterns, FAIR data principles and semantics to enable efficient, compliant, and secure data consumption by humans, systems, and AI agents within and beyond the primary data context. \nLead the collaboration with infrastructure experts to ensure a robust, efficient, secure and fit-for-purpose setup, leveraging infrastructure-as-code and composable architecture. \nMentor and coach less-experienced colleagues and uphold high standards in requirements specification, engineering practices, code review, testing, and documentation. \nActively exchange with other data-related initiatives across Pharma to maintain a comprehensive view of the data landscape and keep strategic alignment on data architecture. \nWymagania\nQualifications and Competencies \nRequired: \nBachelor’s Degree in computer science, information science, knowledge engineering, computational linguistics, data engineering, information technology, or a related field\n5+ years of experience in data architecture, or a similar role, with proven leadership in designing and delivering production-grade data pipelines and services\nStrong ownership of deliverables, high standards for code review and peer mentorship, and a commitment to clear documentation, metadata/lineage publishing, and sustainable data engineering standards and practices\nProficiency in designing and implementing data models (conceptual, logical, and physical)\nProficiency with Databricks, including DBX Asset Bundles, Lakeflow Jobs, Unity Catalog, Access control and governance model and Databricks CLI\nExtensive experience with API design patterns and Ability to integrate data from source systems using various interface patterns, such as OpenSharing/Delta Sharing, Iceberg-Sharing, ODBC, GraphQL, OData, REST, or event streaming\nStrong engineering skills (data engineering, software engineering, or cloud engineering)\nProficiency in Python programming, especially PySpark and Kapitan\nProficiency in Git, including Github Actions, CI/CD Pipelines (declarative YAML), Branching strategies, and trunk-based development\nHands-on experience with Infrastructure-as-code, ideally Terraform on AWS\nIn-depth knowledge of FAIR Data Principles, Linked Data principles, and Data Mesh architecture concepts, with demonstrated ability to apply them in practice\nProficiency with data governance and security principles and practices\nStrong analytical and communication skills\nGood communication and collaboration abilities to work independently in a self-organized team\nHigh level of accuracy and attention to detail\nPreferred \nExperience with GxP validation and Computerized Systems Validation\nExperience in Requirements Engineering\nExperience with semantic layer concepts in modern data platforms such as Databricks, Snowflake, or Microsoft Fabric\nExperience designing and implementing Model Context Protocol (MCP) servers\nHands-on experience with GraphRAG solution design and implementation\nKnowledge of data privacy regulations (e.g., GDPR, CCPA)\nCloud engineering experience (e.g. AWS, Azure)\nKnowledge in the pharmaceutical domain (e.g. LOINC, SNOMED CT, omics, targets, assays, clinical studies, RWE, etc.)\nTech 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